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How to predict The effect on the binding affinity of having a given amino acid at one position can be influenced by the amino acids at other positions in the peptide (sequence correlations). –Two adjacent amino acids may for example compete for the space in a pocket in the MHC molecule. Artificial neural networks (ANN) are ideally suited to take such correlations into account

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How is mutual information calculated? Information content was calculated as Gives information in a single position Similar relation for mutual information Gives mutual information between two positions Mutual information

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Higher order sequence correlations Neural networks can learn higher order correlations! –What does this mean? S S => 0 L S => 1 S L => 1 L L => 0 Say that the peptide needs one and only one large amino acid in the positions P3 and P4 to fill the binding cleft How would you formulate this to test if a peptide can bind? => XOR function

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A Network contains a very large set of parameters –A network with 5 hidden neurons predicting binding for 9meric peptides has 9x20x5=900 weights –5 times as many weights as a matrix-based method Over fitting is a problem Stop training when test performance is optimal (use early stopping) Neural network training years Temperature

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The Wisdom of the Crowds The Wisdom of Crowds. Why the Many are Smarter than the Few. James Surowiecki One day in the fall of 1906, the British scientist Fracis Galton left his home and headed for a country fair… He believed that only a very few people had the characteristics necessary to keep societies healthy. He had devoted much of his career to measuring those characteristics, in fact, in order to prove that the vast majority of people did not have them. … Galton came across a weight-judging competition…Eight hundred people tried their luck. They were a diverse lot, butchers, farmers, clerks and many other no-experts…The crowd had guessed … 1.197 pounds, the ox weighted 1.198

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Network ensembles No one single network with a particular architecture and sequence encoding scheme, will constantly perform the best Also for Neural network predictions will enlightened despotism fail –For some peptides, BLOSUM encoding with a four neuron hidden layer can best predict the peptide/MHC binding, for other peptides a sparse encoded network with zero hidden neurons performs the best –Wisdom of the Crowd Never use just one neural network Use Network ensembles

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What have we learned? Neural networks are not so bad as their reputation Neural networks can deal with higher order correlations Be careful when training a neural network –Always use cross validated training